{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Heart Rate Variability (HRV)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "NeuroKit2 is the most comprehensive package when it comes to HRV indices, and this example shows how to use NeuroKit2 to compute heart rate variability (HRV) indices in the time-, frequency-, and non-linear domain.\n",
                "\n",
                "For a comprehensive review of the most up-to-date HRV indices, a discussion of their significance in psychology, and a step-by-step guide for HRV analysis using **NeuroKit2**, the [Heart Rate Variability in Psychology: A Review of HRV Indices and an Analysis Tutorial](https://doi.org/10.3390/s21123998) paper is a good place to start. "
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Compute HRV features"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {},
            "outputs": [],
            "source": [
                "# Load the NeuroKit package\n",
                "import neurokit2 as nk"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {
                "tags": [
                    "remove-input"
                ]
            },
            "outputs": [],
            "source": [
                "# Note: this cell is hidden using the \"remove-input\" tag\n",
                "# Make bigger images\n",
                "import matplotlib.pyplot as plt\n",
                "plt.rcParams['figure.figsize'] = [15, 5]  \n",
                "plt.rcParams['font.size']= 14"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Download Dataset"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "First, let's download the resting rate data (sampled at 100Hz) using `nk.data()`."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>ECG</th>\n",
                            "      <th>PPG</th>\n",
                            "      <th>RSP</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>0.003766</td>\n",
                            "      <td>-0.102539</td>\n",
                            "      <td>0.494652</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>-0.017466</td>\n",
                            "      <td>-0.103760</td>\n",
                            "      <td>0.502483</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>-0.015679</td>\n",
                            "      <td>-0.107422</td>\n",
                            "      <td>0.511102</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>-0.001598</td>\n",
                            "      <td>-0.110855</td>\n",
                            "      <td>0.518791</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>4</th>\n",
                            "      <td>0.002483</td>\n",
                            "      <td>-0.112610</td>\n",
                            "      <td>0.528669</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "        ECG       PPG       RSP\n",
                            "0  0.003766 -0.102539  0.494652\n",
                            "1 -0.017466 -0.103760  0.502483\n",
                            "2 -0.015679 -0.107422  0.511102\n",
                            "3 -0.001598 -0.110855  0.518791\n",
                            "4  0.002483 -0.112610  0.528669"
                        ]
                    },
                    "execution_count": 3,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "data = nk.data(\"bio_resting_5min_100hz\")\n",
                "data.head()  # Print first 5 rows"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "You can see that it consists of three different signals, pertaining to ECG, PPG (an alternative determinant of heart rate as compared to ECG), and RSP (respiration). Now, let's extract the ECG signal in the shape of a vector (i.e., a one-dimensional array), and find the peaks using [ecg_peaks()](https://neuropsychology.github.io/NeuroKit/functions/ecg.html#ecg-peaks)."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {},
            "outputs": [],
            "source": [
                "# Find peaks\n",
                "peaks, info = nk.ecg_peaks(data[\"ECG\"], sampling_rate=100)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "*Note: It is critical that you specify the correct sampling rate of your signal throughout many processing functions, as this allows NeuroKit to have a time reference.*"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "This produces two elements, `peaks` which is a DataFrame of same length as the input signal in which occurences of R-peaks are marked with 1 in a list of zeros. `info` is a dictionary of the sample points at which these R-peaks occur. \n",
                "\n",
                "HRV is the temporal variation between consecutive heartbeats (**RR intervals**). Here, we will use `peaks` i.e. occurrences of the heartbeat peaks, as the input argument in the following HRV functions to extract the indices. "
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Time-Domain Analysis"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "First, let's extract the time-domain indices."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>HRV_MeanNN</th>\n",
                            "      <th>HRV_SDNN</th>\n",
                            "      <th>HRV_SDANN1</th>\n",
                            "      <th>HRV_SDNNI1</th>\n",
                            "      <th>HRV_SDANN2</th>\n",
                            "      <th>HRV_SDNNI2</th>\n",
                            "      <th>HRV_SDANN5</th>\n",
                            "      <th>HRV_SDNNI5</th>\n",
                            "      <th>HRV_RMSSD</th>\n",
                            "      <th>HRV_SDSD</th>\n",
                            "      <th>HRV_CVNN</th>\n",
                            "      <th>HRV_CVSD</th>\n",
                            "      <th>HRV_MedianNN</th>\n",
                            "      <th>HRV_MadNN</th>\n",
                            "      <th>HRV_MCVNN</th>\n",
                            "      <th>HRV_IQRNN</th>\n",
                            "      <th>HRV_pNN50</th>\n",
                            "      <th>HRV_pNN20</th>\n",
                            "      <th>HRV_HTI</th>\n",
                            "      <th>HRV_TINN</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>696.395349</td>\n",
                            "      <td>62.135891</td>\n",
                            "      <td>10.060728</td>\n",
                            "      <td>60.275036</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>69.697983</td>\n",
                            "      <td>69.779109</td>\n",
                            "      <td>0.089225</td>\n",
                            "      <td>0.100084</td>\n",
                            "      <td>690.0</td>\n",
                            "      <td>44.478</td>\n",
                            "      <td>0.064461</td>\n",
                            "      <td>60.0</td>\n",
                            "      <td>14.651163</td>\n",
                            "      <td>49.302326</td>\n",
                            "      <td>7.962963</td>\n",
                            "      <td>234.375</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "   HRV_MeanNN   HRV_SDNN  HRV_SDANN1  HRV_SDNNI1  HRV_SDANN2  HRV_SDNNI2  \\\n",
                            "0  696.395349  62.135891   10.060728   60.275036         NaN         NaN   \n",
                            "\n",
                            "   HRV_SDANN5  HRV_SDNNI5  HRV_RMSSD   HRV_SDSD  HRV_CVNN  HRV_CVSD  \\\n",
                            "0         NaN         NaN  69.697983  69.779109  0.089225  0.100084   \n",
                            "\n",
                            "   HRV_MedianNN  HRV_MadNN  HRV_MCVNN  HRV_IQRNN  HRV_pNN50  HRV_pNN20  \\\n",
                            "0         690.0     44.478   0.064461       60.0  14.651163  49.302326   \n",
                            "\n",
                            "    HRV_HTI  HRV_TINN  \n",
                            "0  7.962963   234.375  "
                        ]
                    },
                    "execution_count": 5,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 1080x648 with 1 Axes>"
                        ]
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# Extract clean EDA and SCR features\n",
                "hrv_time = nk.hrv_time(peaks, sampling_rate=100, show=True)\n",
                "hrv_time"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "These features include the RMSSD (square root of the mean of the sum of successive differences between adjacent RR intervals), MeanNN (mean of RR intervals) so on and so forth. You can also visualize the distribution of R-R intervals by specifying `show=True` in [hrv_time()](https://neuropsychology.github.io/NeuroKit/functions/hrv.html#hrv-time)."
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Frequency-Domain Analysis"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Now, let's extract the frequency domain features, which involve extracting for example the spectral power density pertaining to different frequency bands. Again, you can visualize the power across frequency bands by specifying `show=True` in [hrv_frequency()](https://neuropsychology.github.io/NeuroKit/functions/hrv.html#hrv-frequency)."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 9,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>HRV_ULF</th>\n",
                            "      <th>HRV_VLF</th>\n",
                            "      <th>HRV_LF</th>\n",
                            "      <th>HRV_HF</th>\n",
                            "      <th>HRV_VHF</th>\n",
                            "      <th>HRV_LFHF</th>\n",
                            "      <th>HRV_LFn</th>\n",
                            "      <th>HRV_HFn</th>\n",
                            "      <th>HRV_LnHF</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>NaN</td>\n",
                            "      <td>0.016913</td>\n",
                            "      <td>0.050186</td>\n",
                            "      <td>0.06632</td>\n",
                            "      <td>0.009835</td>\n",
                            "      <td>0.75672</td>\n",
                            "      <td>0.350328</td>\n",
                            "      <td>0.462957</td>\n",
                            "      <td>-2.713264</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "   HRV_ULF   HRV_VLF    HRV_LF   HRV_HF   HRV_VHF  HRV_LFHF   HRV_LFn  \\\n",
                            "0      NaN  0.016913  0.050186  0.06632  0.009835   0.75672  0.350328   \n",
                            "\n",
                            "    HRV_HFn  HRV_LnHF  \n",
                            "0  0.462957 -2.713264  "
                        ]
                    },
                    "execution_count": 9,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 1080x648 with 1 Axes>"
                        ]
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": [
                "hrv_freq = nk.hrv_frequency(peaks, sampling_rate=100, show=True, normalize=True)\n",
                "hrv_freq"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Non-Linear Domain Analysis"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Now, let's compute the non-linear indices with [hrv_nonlinear()](https://neuropsychology.github.io/NeuroKit/functions/hrv.html#hrv-nonlinear)."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 7,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>HRV_SD1</th>\n",
                            "      <th>HRV_SD2</th>\n",
                            "      <th>HRV_SD1SD2</th>\n",
                            "      <th>HRV_S</th>\n",
                            "      <th>HRV_CSI</th>\n",
                            "      <th>HRV_CVI</th>\n",
                            "      <th>HRV_CSI_Modified</th>\n",
                            "      <th>HRV_PIP</th>\n",
                            "      <th>HRV_IALS</th>\n",
                            "      <th>HRV_PSS</th>\n",
                            "      <th>...</th>\n",
                            "      <th>HRV_SampEn</th>\n",
                            "      <th>HRV_ShanEn</th>\n",
                            "      <th>HRV_FuzzyEn</th>\n",
                            "      <th>HRV_MSE</th>\n",
                            "      <th>HRV_CMSE</th>\n",
                            "      <th>HRV_RCMSE</th>\n",
                            "      <th>HRV_CD</th>\n",
                            "      <th>HRV_HFD</th>\n",
                            "      <th>HRV_KFD</th>\n",
                            "      <th>HRV_LZC</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>49.341281</td>\n",
                            "      <td>72.597435</td>\n",
                            "      <td>0.679656</td>\n",
                            "      <td>11253.343336</td>\n",
                            "      <td>1.471333</td>\n",
                            "      <td>4.758252</td>\n",
                            "      <td>427.259889</td>\n",
                            "      <td>0.55814</td>\n",
                            "      <td>0.511688</td>\n",
                            "      <td>0.736041</td>\n",
                            "      <td>...</td>\n",
                            "      <td>1.259931</td>\n",
                            "      <td>4.282683</td>\n",
                            "      <td>1.063533</td>\n",
                            "      <td>1.166172</td>\n",
                            "      <td>1.331672</td>\n",
                            "      <td>1.387204</td>\n",
                            "      <td>0.712083</td>\n",
                            "      <td>1.921677</td>\n",
                            "      <td>1.913071</td>\n",
                            "      <td>0.854475</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "<p>1 rows × 48 columns</p>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "     HRV_SD1    HRV_SD2  HRV_SD1SD2         HRV_S   HRV_CSI   HRV_CVI  \\\n",
                            "0  49.341281  72.597435    0.679656  11253.343336  1.471333  4.758252   \n",
                            "\n",
                            "   HRV_CSI_Modified  HRV_PIP  HRV_IALS   HRV_PSS  ...  HRV_SampEn  HRV_ShanEn  \\\n",
                            "0        427.259889  0.55814  0.511688  0.736041  ...    1.259931    4.282683   \n",
                            "\n",
                            "   HRV_FuzzyEn   HRV_MSE  HRV_CMSE  HRV_RCMSE    HRV_CD   HRV_HFD   HRV_KFD  \\\n",
                            "0     1.063533  1.166172  1.331672   1.387204  0.712083  1.921677  1.913071   \n",
                            "\n",
                            "    HRV_LZC  \n",
                            "0  0.854475  \n",
                            "\n",
                            "[1 rows x 48 columns]"
                        ]
                    },
                    "execution_count": 7,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 576x576 with 3 Axes>"
                        ]
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": [
                "hrv_nonlinear = nk.hrv_nonlinear(peaks, sampling_rate=100, show=True)\n",
                "hrv_nonlinear"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "This will produce a Poincaré plot which plots each RR interval against the next successive one.\n",
                "\n",
                "Note that there exist many more [**complexity indices**](https://neuropsychology.github.io/NeuroKit/functions/complexity.html), that are available in NeuroKit2, and that could be applied to HRV. The ``hrv_nonlinear()`` function only includes the most commonly used indices."
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## All Domains"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Finally, if you'd like to extract HRV indices from all three domains, you can simply input `peaks` into [hrv()](https://neuropsychology.github.io/NeuroKit/functions/hrv.html), where you can specify `show=True` to visualize the combination of plots depicting the RR intervals distribution, power spectral density for frequency domains, and the poincare scattergram."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe tbody tr th {\n",
                            "        vertical-align: top;\n",
                            "    }\n",
                            "\n",
                            "    .dataframe thead th {\n",
                            "        text-align: right;\n",
                            "    }\n",
                            "</style>\n",
                            "<table border=\"1\" class=\"dataframe\">\n",
                            "  <thead>\n",
                            "    <tr style=\"text-align: right;\">\n",
                            "      <th></th>\n",
                            "      <th>HRV_MeanNN</th>\n",
                            "      <th>HRV_SDNN</th>\n",
                            "      <th>HRV_SDANN1</th>\n",
                            "      <th>HRV_SDNNI1</th>\n",
                            "      <th>HRV_SDANN2</th>\n",
                            "      <th>HRV_SDNNI2</th>\n",
                            "      <th>HRV_SDANN5</th>\n",
                            "      <th>HRV_SDNNI5</th>\n",
                            "      <th>HRV_RMSSD</th>\n",
                            "      <th>HRV_SDSD</th>\n",
                            "      <th>...</th>\n",
                            "      <th>HRV_SampEn</th>\n",
                            "      <th>HRV_ShanEn</th>\n",
                            "      <th>HRV_FuzzyEn</th>\n",
                            "      <th>HRV_MSE</th>\n",
                            "      <th>HRV_CMSE</th>\n",
                            "      <th>HRV_RCMSE</th>\n",
                            "      <th>HRV_CD</th>\n",
                            "      <th>HRV_HFD</th>\n",
                            "      <th>HRV_KFD</th>\n",
                            "      <th>HRV_LZC</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>696.395349</td>\n",
                            "      <td>62.135891</td>\n",
                            "      <td>10.060728</td>\n",
                            "      <td>60.275036</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>69.697983</td>\n",
                            "      <td>69.779109</td>\n",
                            "      <td>...</td>\n",
                            "      <td>1.259931</td>\n",
                            "      <td>4.282683</td>\n",
                            "      <td>1.063533</td>\n",
                            "      <td>1.166172</td>\n",
                            "      <td>1.331672</td>\n",
                            "      <td>1.387204</td>\n",
                            "      <td>0.712083</td>\n",
                            "      <td>1.921677</td>\n",
                            "      <td>1.913071</td>\n",
                            "      <td>0.854475</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "<p>1 rows × 77 columns</p>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "   HRV_MeanNN   HRV_SDNN  HRV_SDANN1  HRV_SDNNI1  HRV_SDANN2  HRV_SDNNI2  \\\n",
                            "0  696.395349  62.135891   10.060728   60.275036         NaN         NaN   \n",
                            "\n",
                            "   HRV_SDANN5  HRV_SDNNI5  HRV_RMSSD   HRV_SDSD  ...  HRV_SampEn  HRV_ShanEn  \\\n",
                            "0         NaN         NaN  69.697983  69.779109  ...    1.259931    4.282683   \n",
                            "\n",
                            "   HRV_FuzzyEn   HRV_MSE  HRV_CMSE  HRV_RCMSE    HRV_CD   HRV_HFD   HRV_KFD  \\\n",
                            "0     1.063533  1.166172  1.331672   1.387204  0.712083  1.921677  1.913071   \n",
                            "\n",
                            "    HRV_LZC  \n",
                            "0  0.854475  \n",
                            "\n",
                            "[1 rows x 77 columns]"
                        ]
                    },
                    "execution_count": 8,
                    "metadata": {},
                    "output_type": "execute_result"
                },
                {
                    "data": {
                        "image/png": 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",
                        "text/plain": [
                            "<Figure size 1080x648 with 5 Axes>"
                        ]
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": [
                "hrv_indices = nk.hrv(peaks, sampling_rate=100, show=True)\n",
                "hrv_indices"
            ]
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3",
            "language": "python",
            "name": "python3"
        },
        "language_info": {
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
            },
            "file_extension": ".py",
            "mimetype": "text/x-python",
            "name": "python",
            "nbconvert_exporter": "python",
            "pygments_lexer": "ipython3",
            "version": "3.7.1"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 2
}
